Ten years is a long time in any industry. In AI, it's several lifetimes.
When Infrrd started in 2016, the word "agentic" didn't exist in anyone's vocabulary. Large language models were academic papers, not products. And intelligent document processing was a category that hadn't been named yet. We were building in the dark, figuring things out as we went, making mistakes we didn't see coming, and occasionally stumbling onto breakthroughs we didn't expect.
A decade later, we've processed hundreds of millions of documents, built technology that didn't exist when we started, and learned lessons that no textbook could have taught us.
Here are ten of them. The honest ones.
1. What We Learned About Hiring the Hard Way
We made a lot of hiring mistakes early on. Most of them were fixable. One kind wasn't.
The hardest people to let go were never the ones who couldn't do the work. They were the ones who were exceptional at the work but impossible to work with. The brilliant jerks. Customers loved them. Deliverables were never late. But something else was happening underneath, the team walked on eggshells. Collaboration quietly broke down. People stopped speaking up. The hidden cost was enormous, and it didn't show up on any dashboard.
The first time we had to let someone like this go, it was genuinely painful. How do you let go of someone who lives and breathes the work? Who's outperforming everyone by every measurable metric?
You do it because culture isn't a poster on the wall. It's what you protect when it's expensive to do so. The lesson: hire for attitude as hard as you hire for ability. Skills can be developed. Character is harder to change.
2. "Nothing Is Impossible" Is Not a Slogan. It's an Operating System.
AI agents and agentic AI feel like brand new ideas. Every conference, every LinkedIn post, every funding announcement is talking about them like they were invented last year.
Infrrd was built on that exact reasoning a decade ago.
When we started, the mainstream was OCR/optical character recognition. Basic. Brittle. Limited. We started with a different idea: what if you could process and extract anything from any document, regardless of format, structure, or complexity?
That sounded impossible to most people at the time. It drove our roadmap anyway.
The companies now racing to catch up with agentic AI are discovering what we spent ten years building toward. The mindset came first. The technology followed. When you genuinely believe something is possible, you don't wait for the industry to validate it. You build it.
3. Customers Don't Buy What They Can't Experience
For the first year at Infrrd, we had a demo problem we couldn't see.
Every first call, we'd show prospects our pre-trained model — genuinely impressive accuracy, and then ask them to send us 200 documents before we could prove it would work for them. Not 5. Not 10. Two hundred documents.
Deals weren't closing. We couldn't understand why. Our accuracy was better than anyone else in the market. Then one of our advisors watched the demo. His feedback was blunt: it was the worst product demo he'd ever seen. That stung. But it cracked something open.
We spent the next three months rebuilding everything: the storyline, the algorithms, the experience. The new demo opened with a single moment: we asked the customer to upload their own document, live on the call, with zero training and zero preparation. And it worked. Right there. In real time.
That one moment did more for our pipeline than two years of accuracy benchmarks ever had.
The lesson is simple and brutal: customers don't buy what they cannot experience. They buy confidence that this will solve their problem. The burden of proof sits on you , not on the customer's patience or imagination.
4. 90% Accuracy Isn't Good Enough. We Had to Invent Certainty.
90% accuracy sounds impressive, until you do the math. It means a 1-in-10 chance the data point is wrong. For most businesses processing thousands of documents a day, that's not a margin of error. That's a liability.
The entire document processing industry had been selling customers a polished version of uncertainty. Tolerance bands dressed up as confidence.
We were doing the same thing.
In 2020, a customer changed that. They told us that if they had to review every document — even if they made no changes after review, their ROI wouldn't work. They needed guaranteed accuracy. No review. No safety net. Just certainty.
We couldn't deliver it at the time. But we didn't walk away from the problem either. We handed it to our research team.
Two years. Countless failed experiments. Long nights that bled into early mornings. What came out the other side was something we now call AI Certainty — the ability to know, not estimate, when an extraction is correct.
Today, when we process a document, we tell customers: don't open it for review. We've got it.
The lesson: your most demanding customers aren't problems to manage. They're showing you where the industry needs to go.
5. When Your Data Team Walks Out, They Take Your Institutional Memory With Them
This one doesn't get talked about enough.
When experienced data entry workers leave, and they always eventually leave — they take with them years of accumulated knowledge. The edge cases they knew how to handle. The exceptions they caught by instinct. The context that never made it into any documentation.
New hires start from scratch. Errors creep back in. Processes that ran smoothly start showing cracks. This is one of the most underestimated arguments for document automation. It's not just about speed or cost reduction. It's about building a system that retains what your best people know , and doesn't lose it every time someone moves on.
Data stability isn't a technical problem. It's a people problem that technology can solve.
6. We Taught AI to Teach Itself
While most of the industry was still experimenting with basic prompt adjustments, we were building something different: a multi-agentic framework where multiple AI models continuously interact, challenge, and learn from each other.
The result is a self-improving system that doesn't need constant human intervention to get better. Specialized agents handle tasks based on complexity. Rules are optimized automatically. The system distinguishes between similar entities, avoids overfitting, and maintains accuracy over time through a continuous feedback loop.
The short version: we built AI that teaches AI. The lesson here isn't about the technology. It's about the ambition. When you're building in a fast-moving space, incremental improvements aren't enough. You have to be willing to rethink the architecture entirely.
7. True Automation Means Never Having to Double-Check
Here's the problem with most document processing systems that nobody talks about: they tell you something went wrong, but not what.
You feed in 100 documents. The system says 70 were processed successfully, and 30 had issues. Great. But which 30? Your team still has to manually review all 100 to find out. You've automated the processing but not the judgment. You've just moved the burden around.
That's not intelligent automation. That's expensive theatre.
Infrrd's No-Touch Processing works differently. When 100 documents come in, the system doesn't just process them , it understands them. It flags exactly which 30 need a human eye, and confidently marks the other 70 as accurate and ready to move forward. Those 70 are done. No one needs to touch them again.
That's the standard we built toward: automation you can actually trust. Not automation, you have to babysit.
8. Data Extraction Was Never the Finish Line
For years, we were an IDP company. Intelligent Document Processing. Extract the data, deliver it clean, move on.
Then we started listening more carefully to our customers. The data wasn't the problem. What happened after the data was the problem. The handoffs, the decisions, the downstream workflows that still required humans to connect the dots between what the document said and what the business needed to do next.
That conversation led to Ally - Infrrd's Agentic AI. A system that doesn't just extract data but acts on it. That takes the output of document processing and drives the next step in the workflow autonomously.
The lesson: listen to what your customers are actually struggling with, not just the problem you were hired to solve. The real opportunity is usually one step further than where you stopped.
9. The Wave Is Here. You Can Resist It or Ride It.
People consistently underestimate what AI can already do. Even people who work in technology. Even people who understand the models. There's a deeply human tendency to believe that your domain, your job, your expertise, your judgment — is too nuanced, too contextual, too human to be touched.
That's not cynicism. It's pattern recognition. Every wave of disruption looks obvious in hindsight and implausible in the moment. The knowledge industry is next. Finance teams, legal, mortgage, insurance, healthcare. AI is not coming for the jobs that require human judgment. It's coming for the hours of grunt work that precede that judgment. The backlog clearing, the document reviewing, the data entry, and the first-pass analysis.
At Infrrd, we're seeing 15x productivity gains in the workflows we've automated. That's not a rounding error. That's a structural shift.
The lesson isn't to be afraid. It's too early. If AI can take the first shift, imagine what you can do with the second.
10. Ten Years Down. Light-Years Ahead.
A decade ago, we were a small team with a big idea and no guarantee it would work.
Today, we've built technology that didn't exist when we started. We've served customers who pushed us further than we thought we could go. We've hired people who became the culture, not just contributors to it. We've failed in ways that taught us more than our wins did.
Ten years in, we're not reflecting because we're slowing down. We're reflecting because looking back clearly is the only way to move forward honestly.
The next decade of data intelligence is going to make the last one look like the warm-up. AI is getting faster, smarter, and more capable of handling complexity that previously required humans. The businesses that thrive won't be the ones that wait to see how it plays out. They'll be the ones who decided early what kind of future they were building toward and started building.
We made that decision ten years ago.
